1. Field of the Invention
The present invention relates to an image processing apparatus and a computer readable media containing an image processing program for detecting an abnormal shadow in a medical image.
2. Description of the Related Art
In recent years, computer aided diagnosis (CAD) has been used in the medical field. In the CAD, a digital medical image (hereinafter simply referred to as a medical image) is analyzed for diagnosis through the use of a computer to reduce burden on a person who interprets the image, such as a doctor, during imaging diagnosis and to improve accuracy of diagnosis.
A known CAD scheme involves, for example, automatically analyzing malignancy of a tumor based on image information of the size of a lesion, and the like, in the medical image. It is necessary for medical diagnosis that a tumor region or a contour of the tumor region is accurately detected from a CT image, or the like, based on image information of a site where such a tumor is present.
U.S. Pat. No. 6,738,499 has proposed a method in which the contour of a tumor is manually set by an expert such as a radiologist, and likelihood of malignancy is automatically analyzed through an artificial neural network (ANN) technique based on feature quantities of the tumor obtained from the contour, such as the size of the tumor and a CT value distribution within the tumor.
As a highly robust method for detecting a tumor, a method has been proposed in K. Okada et al., “Robust Anisotropic Gaussian Fitting for Volumetric Characterization of Pulmonary Nodules in Multislice CT”, IEEE Trans. Medical Imaging, Vol. 24, No. 3, pp. 409-423, 2005. In this method, it is supposed that a tumor region has a Gaussian luminance distribution, and the mean and variance estimates of anisotropic Gaussian are calculated to find a Gaussian distribution that fits the best to the luminance distribution of the tumor region, to obtained an elliptic contour having the position of the median of the Gaussian distribution as the center. In this method, the user specifies a point in the tumor region, and an area around the point is searched with gradually changing the position of the point to find an optimal contour having a luminance distribution similar to the Gaussian distribution.
However, both of the above methods require the user to specify an approximate position of the tumor. Since the same tumor of the same subject often appears at the same position in medical images of the subject taken at different times for observing changes of the tumor over time, it is troublesome for the user to specify the position of the tumor every time.